SORS: A Modular, High-Fidelity Simulator for Soft Robots

• URL: http://arxiv.org/abs/2512.15994v1
• Date: Wed, 17 Dec 2025 21:58:46 GMT
• Title: SORS: A Modular, High-Fidelity Simulator for Soft Robots
• Abstract summary: Soft robots pose unique modeling challenges due to their large nonlinear deformations, material incompressibility, and contact interactions.<n>We present SORS, a versatile, high-fidelity simulator designed to handle these complexities for soft robot applications.
• Score: 5.660024841218955
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The deployment of complex soft robots in multiphysics environments requires advanced simulation frameworks that not only capture interactions between different types of material, but also translate accurately to real-world performance. Soft robots pose unique modeling challenges due to their large nonlinear deformations, material incompressibility, and contact interactions, which complicate both numerical stability and physical accuracy. Despite recent progress, robotic simulators often struggle with modeling such phenomena in a scalable and application-relevant manner. We present SORS (Soft Over Rigid Simulator), a versatile, high-fidelity simulator designed to handle these complexities for soft robot applications. Our energy-based framework, built on the finite element method, allows modular extensions, enabling the inclusion of custom-designed material and actuation models. To ensure physically consistent contact handling, we integrate a constrained nonlinear optimization based on sequential quadratic programming, allowing for stable and accurate modeling of contact phenomena. We validate our simulator through a diverse set of real-world experiments, which include cantilever deflection, pressure-actuation of a soft robotic arm, and contact interactions from the PokeFlex dataset. In addition, we showcase the potential of our framework for control optimization of a soft robotic leg. These tests confirm that our simulator can capture both fundamental material behavior and complex actuation dynamics with high physical fidelity. By bridging the sim-to-real gap in these challenging domains, our approach provides a validated tool for prototyping next-generation soft robots, filling the gap of extensibility, fidelity, and usability in the soft robotic ecosystem.

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